Inference of functional relations in predicted protein networks with a machine learning approach

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dc.contributor.author García-Jiménez, Beatriz
dc.contributor.author Juan, David
dc.contributor.author Ezkurdia, Iakes
dc.contributor.author Andrés León, Eduardo
dc.contributor.author Valencia, Alfonso
dc.date.accessioned 2010-11-29T11:44:48Z
dc.date.available 2010-11-29T11:44:48Z
dc.date.issued 2010-04
dc.identifier.bibliographicCitation PLoS ONE, 2010, vol. 5, nº 4, p. 1-10.
dc.identifier.issn 1932-6203
dc.identifier.uri http://hdl.handle.net/10016/9734
dc.description.abstract Background: Molecular biology is currently facing the challenging task of functionally characterizing the proteome. The large number of possible protein-protein interactions and complexes, the variety of environmental conditions and cellular states in which these interactions can be reorganized, and the multiple ways in which a protein can influence the function of others, requires the development of experimental and computational approaches to analyze and predict functional associations between proteins as part of their activity in the interactome. Methodology/Principal Findings: We have studied the possibility of constructing a classifier in order to combine the output of the several protein interaction prediction methods. The AODE (Averaged One-Dependence Estimators) machine learning algorithm is a suitable choice in this case and it provides better results than the individual prediction methods, and it has better performances than other tested alternative methods in this experimental set up. To illustrate the potential use of this new AODE-based Predictor of Protein InterActions (APPIA), when analyzing high-throughput experimental data, we show how it helps to filter the results of published High-Throughput proteomic studies, ranking in a significant way functionally related pairs. Availability: All the predictions of the individual methods and of the combined APPIA predictor, together with the used datasets of functional associations are available at http://ecid.bioinfo.cnio.es/. Conclusions: We propose a strategy that integrates the main current computational techniques used to predict functional associations into a unified classifier system, specifically focusing on the evaluation of poorly characterized protein pairs. We selected the AODE classifier as the appropriate tool to perform this task. AODE is particularly useful to extract valuable information from large unbalanced and heterogeneous data sets. The combination of the information provided by five prediction interaction prediction methods with some simple sequence features in APPIA is useful in establishing reliability values and helpful to prioritize functional interactions that can be further experimentally characterized.
dc.description.sponsorship This work was funded by the BioSapiens (grant number LSHG-CT-2003-503265) and the Experimental Network for Functional Integration (ENFIN) Networks of Excellence (contract number LSHG-CT-2005-518254), by Consolider BSC (grant number CSD2007-00050) and by the project “Functions for gene sets” from the Spanish Ministry of Education and Science (BIO2007-66855). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Public Library of Science (PLoS)
dc.rights © 2010 García-Jiménez et al.
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.title Inference of functional relations in predicted protein networks with a machine learning approach
dc.type article
dc.relation.publisherversion http://dx.doi.org/10.1371/journal.pone.0009969
dc.subject.eciencia Informática
dc.identifier.doi 10.1371/journal.pone.0009969
dc.rights.accessRights openAccess
dc.identifier.publicationfirstpage 1
dc.identifier.publicationissue 4
dc.identifier.publicationlastpage 10
dc.identifier.publicationtitle PLoS ONE
dc.identifier.publicationvolume 5
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